Salīdzināt metodes

	Aktīvās mācīšanās autoenkodera anomāliju noteikšana	Daļēji uzraudzīts autoenkoders anomaliju noteikšanai
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2014–2018	2018–2020
Autors	Multiple (Guo et al.; Pimentel et al.)	Ruff, L. et al.; Zong, B. et al.
Tips	Active learning + unsupervised deep anomaly detection hybrid	Semi-supervised deep anomaly detection
Pirmavots	Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215–249.	Ruff, L., Vandermeulen, R. A., Franks, B. J., Müller, K.-R., & Kloft, M. (2020). Deep Semi-Supervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2020).
Citi nosaukumi	AL-Autoencoder anomaly detection, active autoencoder anomaly detection, query-guided autoencoder anomaly detection, active deep anomaly detection	Semi-supervised AE anomaly detection, SSAD autoencoder, semi-supervised reconstruction-error detection, partially labeled autoencoder anomaly detection
Saistītās	6	5
Kopsavilkums	Active Learning Autoencoder Anomaly Detection combines an autoencoder's unsupervised reconstruction-error scoring with an active learning query loop. The model flags high-error instances as candidate anomalies, selectively asks a human oracle to label the most informative ones, and iteratively retrains — achieving strong anomaly detection with only a small labeling budget.	Semi-supervised Autoencoder Anomaly Detection trains a neural autoencoder primarily on normal (unlabeled) data, then uses a small set of labeled anomalies to refine decision boundaries, detecting anomalies as samples with high reconstruction error. It bridges the gap between purely unsupervised autoencoders and fully supervised classifiers when labels are scarce but some known anomalies exist.